Liang Xingdong

A novel 3-D reconstruction approach based on group-sparsity of array InSAR

As an advanced technology, synthetic aperture radar tomography (Tomo SAR) provides the feasibility to solve the layover problem caused by the inherent side-view mode in SAR sensors. However, the conventional nonparametric spectral estimation methods, such as truncated singular value decomposition (TSVD), are limited by the poor elevation resolution and cannot meet the needs of practical applications. Currently, SAR 3-D imaging methods based on compressive sensing are typically used; nevertheless, classic compressive sensing algorithms such as BP and OMP still exhibitproblems such as low efficiency, weak super-resolution performance, and poor anti-interference ability. Therefore, an algorithm with high robustness and super-resolution performance is significantly demanded.In this paper, a novel array InSAR 3-D reconstruction algorithm based on group-sparsity is proposed. It is improved based on the existing compressive sensing algorithm and exhibits better super-resolution and robustness. Based on the simulation data and the actual data of the first domestic 3-D imaging experiment by an airborne array InSAR, the super-resolution ability ofthe algorithm is verified, and the 3-D imaging results of the buildings are obtained.